Article Per Year (5 Year)
p-Index From 2021 - 2026
1.116
P-Index
This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Instal : Jurnal Komputer Jurnal Masyarakat Indonesia Zadama: Jurnal Pengabdian Masyarakat
Nurhayati
Universitas Mikroskil
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Public Health Social Sciences

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
Pelatihan Red Hat OpenShift Development Introduction to Containers with Podman Untuk Mahasiswa Universitas Mikroskil R. A. Fattah Andriansyah; Nurhayati; Arisman Arisman
Educativo: Jurnal Pendidikan Vol 2 No 1 (2023): Zadama: Jurnal Pengabdian Masyarakat
Publisher : PT. Marosk Zada Cemerlang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In line with the development of information technology and communication, Mikroskil University always make efforts to prepare students in order to contribute at Industrial revolution 4.0. One of them has to understand and practice Linux based on networking skill using Red Hat with Podman container engine. Today software development is increasingly using an approach called containerization, where software standard packages known as containers combine application code together with configuration files and associated libraries and with the dependencies needed for applications to run. Analogies such as the shipping industry using physical containers to isolate various cargoes for transport by ship and rail. This enables developers and IT professionals to deploy applications seamlessly across environments with Comparing Containers to Virtual Machines, such as Container virtual machines (VMs), Kubernetes and Red Hat OpenShift Container Platform (RHOCP).
THE APPLICATION OF THE ROUGH SET METHOD TO ANALYZE THE CONTRIBUTION OF MANAGEMENT OF SCIENCE LABORATORY USE TO STUDENTS' SCIENCE PROCESS SKILLS Nurhayati; Baharis Setia Adisahputra Simatupang; R.A Fattah Adrianyah; R L Harmady Tamba
Bahasa Indonesia Vol 15 No 01 (2023): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalkomputer.v15i01.50

Abstract

This study aims to analyze and determine the operational contribution of laboratory management to students' scientific process skills with Data Mining in collecting data from questionnaire instruments using methods rough set into rosetta software to produce a Decision system in the form of Equivalence Class and Dicernibility Matrix Modulo D Then carry out a Reduction process which will be used as a reference for making General Rules, so as to produce value the highest is SMP N 2 L. Pakam with a percentage of 0.95625 and the result is Good.
Upaya Peningkatan Kelola Keuangan di Kantin Dinas Pertanian Kabupaten Langkat Nurhayati; Arisman; Frans Mikael Sinaga; Ronald Belferik; Tuti Andriani; Irfan Nainggolan; Suhendra Simangunsong
Jurnal Masyarakat Indonesia (Jumas) Vol. 4 No. 03 (2025): Jurnal Masyarakat Indonesia (Jumas)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The canteen is one of the key facilities that supports the activities of employees as well as visitors in government institutions. However, a frequent issue encountered is financial management, which is still recorded manually in notebooks and not properly documented. This community service program was conducted at the Canteen of the Department of Agriculture, Langkat Regency, with the aim of providing guidance in manual financial recording, cash flow management, and monthly financial reporting. The methods applied in this program included observation, socialization, training, and evaluation. The results indicated an improvement in the manager’s ability to prepare daily and monthly financial reports, understand income and expenditure flows, and implement a recording system using Microsoft Excel. Through this program, it is expected that the canteen managers will be able to maintain transparency and accountability, thereby ensuring the sustainability of the canteen’s operations effectively.
Pelatihan dan Pendampingan Digital Marketing bagi Pelaku Usaha Rumah Makan untuk Meningkatkan Daya Saing Nurhayati; Arisman; Nuraina; Suminar Ariwibowo; Nanda felani Baihaqi; Syahrial Sitorus
Jurnal Masyarakat Indonesia (Jumas) Vol. 5 No. 01 (2026): Jurnal Masyarakat Indonesia (Jumas)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jumas.v5i01.359

Abstract

The culinary business sector, especially small and medium-scale restaurants, faces increasingly competitive challenges in the digital era. Many restaurant business owners still rely on conventional promotional methods and have not optimally utilized digital technology to expand market reach and improve competitiveness. This community service activity was carried out with the aim of providing training and mentoring related to digital marketing strategies for restaurant business actors. The methods used in this activity include observation, socialization, training, mentoring, and evaluation. The training materials covered the use of social media, digital content creation, online promotion strategies, customer engagement, and the utilization of digital platforms such as Instagram, WhatsApp Business, Google Maps, and food delivery applications. The results of this activity showed an increase in participants’ understanding and skills in managing digital promotions, creating attractive content, and utilizing online platforms to support business development. Through this activity, restaurant business actors are expected to be able to improve service quality, expand market reach, and strengthen business competitiveness in the digital era.
INDOBERTWEET DENGAN TEMPORAL ATTENTION MECHANISM UNTUK DETEKSI ISU BENCANA DINAMIS MULTIPLATFORM Nurhayati; Tanti; Nuraina; Arisman; Felix
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/qtam3c42

Abstract

Abstract: The development of disaster-related information in the digital space is very rapid and is often detected earlier through social media than through official government channels. This condition highlights the need for a system capable of detecting disaster-related issues quickly and dynamically across various digital platforms. This study aims to develop a dynamic disaster issue detection model based on IndoBERTweet with a Temporal Attention Mechanism using multiplatform big text data in Indonesia. The research methodology includes collecting textual data from various digital platforms such as Twitter, YouTube, TikTok, Quora, and Medium. The data are processed through preprocessing stages using Natural Language Processing (NLP) techniques, timestamp extraction to obtain temporal information, and the application of fine-grained labeling for more detailed classification of disaster-related issues. Subsequently, the IndoBERTweet model is trained with a Temporal Attention Mechanism to capture the relationship between textual context and temporal dynamics in the development of disaster-related issues. The expected results of this research are a model capable of dynamically detecting disaster-related issues by considering informal language contexts and temporal changes. This model is expected to support early warning systems and data-driven disaster management decision-making in Indonesia.   Keywords: Disaster Issue Detection; Social Media Text Analysis; Multiplatform Big Data; IndoBERTweet; Temporal Attention.   Abstrak: Perkembangan informasi kebencanaan di ruang digital berlangsung sangat cepat dan sering kali lebih dahulu terdeteksi melalui media sosial dibandingkan melalui kanal resmi pemerintah. Kondisi ini menunjukkan perlunya sistem yang mampu mendeteksi isu bencana secara cepat dan dinamis dari berbagai platform digital. Penelitian ini bertujuan mengembangkan model deteksi isu bencana dinamis berbasis IndoBERTweet dengan Temporal Attention Mechanism pada big data teks multiplatform di Indonesia. Metode penelitian meliputi pengumpulan data teks dari berbagai platform digital seperti Twitter, YouTube, TikTok, Quora, dan Medium. Data diproses melalui tahapan preprocessing menggunakan teknik Natural Language Processing (NLP), ekstraksi timestamp untuk memperoleh informasi temporal, serta penerapan fine-grained labeling untuk klasifikasi isu bencana yang lebih rinci. Selanjutnya, model IndoBERTweet dilatih dengan Temporal Attention Mechanism untuk menangkap hubungan antara konteks teks dan dinamika waktu dalam perkembangan isu bencana. Hasil penelitian diharapkan menghasilkan model yang mampu mendeteksi isu bencana secara dinamis dengan mempertimbangkan konteks bahasa informal dan perubahan waktu. Model ini diharapkan mendukung sistem peringatan dini dan pengambilan kebijakan kebencanaan berbasis data di Indonesia.   Kata kunci: Deteksi Isu Bencana; Analisis Teks Media Sosial; Big Data Multiplatform; IndoBERTweet; Temporal Attention.
PEMBELAJARAN REPRESENTASI BERBASIS SELF-SUPERVISED UNTUK KLASIFIKASI PENYAKIT PARU PADA CITRA CHEST X-RAY DENGAN DATA BERLABEL TERBATAS Nurhayati; Tioria Pasaribu; Hernawati Gohzali; Fauzi; Arisman; Suminar Ariwibowo
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 3 (2026): June 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i3.6482

Abstract

Abstract: Lung disease classification based on Chest X-Ray (CXR) images has become an important focus in the development of deep learning for medical imaging. However, most modern classification models still rely heavily on large amounts of labeled data, while medical image annotation requires radiology experts, high costs, and considerable time. This study aims to implement a self-supervised learning approach based on contrastive learning for lung disease classification on CXR images using the CheXpert dataset under limited labeled data conditions. The research stages include data preprocessing, image augmentation, self-supervised pretraining, fine-tuning, and model evaluation using accuracy, precision, recall, and F1-score metrics. The dataset was divided into 70% training data and 30% testing data. The experimental results showed that the model achieved an accuracy of 0.87, precision of 0.87, recall of 0.84, and F1-score of 0.85. These results indicate that the self-supervised learning approach is capable of utilizing unlabeled data to generate robust visual representations and improve lung disease classification performance under limited labeled data conditions. This study is expected to contribute to the development of more efficient deep learning-based medical image analysis systems with reduced dependency on medical annotations. Keywords: Chest X-Ray, Self-Supervised Learning, Contrastive Learning, Deep Learning, Lung Disease Classification.   Abstrak: Klasifikasi penyakit paru berbasis citra CXR menjadi salah satu fokus penting dalam pengembangan deep learning pada bidang pencitraan medis. Namun, sebagian besar model klasifikasi modern masih bergantung pada data berlabel dalam jumlah besar, sedangkan proses anotasi citra medis membutuhkan tenaga ahli radiologi, biaya tinggi, dan waktu yang panjang. Penelitian ini bertujuan menerapkan pendekatan self-supervised learning berbasis contrastive learning untuk klasifikasi penyakit paru pada citra CXR menggunakan dataset CheXpert dengan kondisi data berlabel terbatas. Tahapan penelitian meliputi pra-pemrosesan data, augmentasi citra, self-supervised pretraining, fine-tuning, dan evaluasi model menggunakan metrik accuracy, precision, recall, dan F1-score. Dataset dibagi menggunakan rasio 70% data pelatihan dan 30% data pengujian. Hasil penelitian menunjukkan bahwa model mampu menghasilkan nilai accuracy sebesar 0.87, precision 0.87, recall 0.84, dan F1-score 0.85. Hasil tersebut menunjukkan bahwa pendekatan self-supervised learning mampu memanfaatkan data tidak berlabel untuk menghasilkan representasi visual yang robust dan meningkatkan performa klasifikasi penyakit paru pada kondisi data berlabel terbatas. Penelitian ini diharapkan dapat mendukung pengembangan sistem analisis citra medis berbasis deep learning yang lebih efisien terhadap kebutuhan anotasi medis. Kata Kunci: Chest X-Ray, Self-Supervised Learning, Contrastive Learning, Deep Learning, Klasifikasi Penyakit Paru.
Co-Authors Arisman Baharis Setia Adisahputra Simatupang Fauzi Felix Frans Mikael Sinaga Hernawati Gohzali Irfan Nainggolan Nanda felani Baihaqi Nuraina Pasaribu, Tioria R L Harmady Tamba R. A. Fattah Andriansyah R.A Fattah Adrianyah Ronald Belferik Suhendra Simangunsong Suminar Ariwibowo Syahrial Sitorus Tanti Tuti Andriani